Skip to main content

A Python gRPC framework for serving a machine learning module written in Python.

Project description

Rekcurd

Build Status PyPI version codecov pypi supported versions

Rekcurd is the Project for serving ML module. This is a gRPC micro-framework and it can be used like Django and Flask.

Parent Project

https://github.com/rekcurd/community

Components

Installation

From source:

$ git clone --recursive https://github.com/rekcurd/rekcurd-python.git
$ cd rekcurd-python
$ pip install -e .

From PyPi directly:

$ pip install rekcurd

How to use

Example is available here. You can generate Rekcurd template and implement necessary methods.

$ rekcurd startapp {Your application name}
$ cd {Your application name}
$ vi app.py
$ python app.py

Unittest

$ python -m unittest

Kubernetes support

Rekcurd can be run on Kubernetes. See community repository.

Type definition

PredictLabel type

V is the length of feature vector.

Field Type Description
input
(required)
One of below
- string
- bytes
- string[V]
- int[V]
- double[V]
Input data for inference.
- "Nice weather." for a sentiment analysis.
- PNG file for an image transformation.
- ["a", "b"] for a text summarization.
- [1, 2] for a sales forcast.
- [0.9, 0.1] for mnist data.
option string Option field. Must be json format.

The "option" field needs to be a json format. Any style is Ok but we have some reserved fields below.

Field Type Description
suppress_log_input bool True: NOT print the input and output to the log message.
False (default): Print the input and outpu to the log message.
YOUR KEY any YOUR VALUE

PredictResult type

M is the number of classes. If your algorithm is a binary classifier, you set M to 1. If your algorithm is a multi-class classifier, you set M to the number of classes.

Field Type Description
label
(required)
One of below
-string
-bytes
-string[M]
-int[M]
-double[M]
Result of inference.
-"positive" for a sentiment analysis.
-PNG file for an image transformation.
-["a", "b"] for a multi-class classification.
-[1, 2] for a multi-class classification.
-[0.9, 0.1] for a multi-class classification.
score
(required)
One of below
-double
-double[M]
Score of result.
-0.98 for a binary classification.
-[0.9, 0.1] for a multi-class classification.
option string Option field. Must be json format.

EvaluateResult type

EvaluateResult is the evaluation score. N is the number of evaluation data. M is the number of classes. If your algorithm is a binary classifier, you set M to 1. If your algorithm is a multi-class classifier, you set M to the number of classes.

Field Type Description
num
(required)
int Number of evaluation data.
accuracy
(required)
double Accuracy.
precision
(required)
double[M] Precision.
recall
(required)
double[M] Recall.
fvalue
(required)
double[M] F1 value.

EvaluateDetail type

EvaluateDetail is the details of evaluation result.

Field Type Description
result
(required)
PredictResult Prediction result.
is_correct
(required)
bool Correct or not.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for rekcurd, version 1.0.1.post1
Filename, size File type Python version Upload date Hashes
Filename, size rekcurd-1.0.1.post1-py2.py3-none-any.whl (38.7 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size rekcurd-1.0.1.post1.tar.gz (30.7 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page